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Racial disparities affect multiple dimensions of epilepsy care including epilepsy surgery. This study aims to further explore these disparities by determining the utilization of invasive neuromodulation devices according to race and ethnicity in a multicenter study of patients living with focal drug-resistant epilepsy (DRE). We performed a post hoc analysis of the Human Epilepsy Project 2 (HEP2) data. HEP2 is a prospective study of patients living with focal DRE involving 10 sites distributed across the United States. There were no statistical differences in the racial distribution of the study population compared to the US population using census data except for patients reporting more than one race. Of 154 patients enrolled in HEP2, 55 (36%) underwent invasive neuromodulation for DRE management at some point in the course of their epilepsy. Of those, 36 (71%) were patients who identified as White. Patients were significantly less likely to have a device if they identified solely as Black/African American than if they did not (odds ratio = .21, 95% confidence interval = .05-.96, p = .03). Invasive neuromodulation for management of DRE is underutilized in the Black/African American population, indicating a new facet of racial disparities in epilepsy care., (© 2024 The Authors. Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.)

Objectives: The Epilepsy Learning Healthcare System (ELHS) was created in 2018 to address measurable improvements in outcomes for people with epilepsy. However, fragmentation of data systems has been a major barrier for reporting and participation. In this study, we aimed to test the feasibility of an open-source Data Integration (DI) method that connects real-life clinical data to national research and quality improvement (QI) systems., Methods: The ELHS case report forms were programmed as EPIC SmartPhrases at Mass General Brigham (MGB) in December 2018 and subsequently as EPIC SmartForms in June 2021 to collect actionable, standardized, structured epilepsy data in the electronic health record (EHR) for subsequent pull into the external national registry of the ELHS. Following the QI methodology in the Chronic Care Model, 39 providers, epileptologists and neurologists, incorporated the ELHS SmartPhrase into their clinical workflow, focusing on collecting diagnosis of epilepsy, seizure type according to the International League Against Epilepsy, seizure frequency, date of last seizure, medication adherence and side effects. The collected data was stored in the Enterprise Data Warehouse (EDW) without integration with external systems. We developed and validated a DI method that extracted the data from EDW using structured query language and later preprocessed using text mining. We used the ELHS data dictionary to match fields in the preprocessed notes to obtain the final structured dataset with seizure control information. For illustration, we described the data curated from the care period of 12/2018-12/2021., Results: The cohort comprised a total of 1806 patients with a mean age of 43 years old (SD: 17.0), where 57% were female, 80% were white, and 84% were non-Hispanic/Latino. Using our DI method, we automated the data mining, preprocessing, and exporting of the structured dataset into a local database, to be weekly accessible to clinicians and quality improvers. During the period of SmartPhrase implementation, there were 5168 clinic visits logged by providers documenting each patient's seizure type and frequency. During this period, providers documented 59% patients having focal seizures, 35% having generalized seizures and 6% patients having another type. Of the cohort, 45% patients had private insurance. The resulting structured dataset was bulk uploaded via web interface into the external national registry of the ELHS., Conclusions: Structured data can be feasibly extracted from text notes of epilepsy patients for weekly reporting to a national learning healthcare system., Competing Interests: Declarations of Interest None., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Objective: To establish a basis for a domain ontology - a formal, explicit specification of a shared conceptualization - of collaborative learning healthcare systems (CLHSs) in order to facilitate measurement, explanation, and improvement., Methods: We adapted the "Methontology" approach to begin building an ontology of CLHSs. We specified the purpose of an ontology, acquired domain knowledge via literature review, conceptualized a common framework of CLHSs using a grounded approach, refined these concepts based on expert panel input, and illustrated concept application via four cases., Results: The set of concepts identified as important to include in an ontology includes goals, values, structure, actors, environment, and products. To establish this set of concepts, we gathered input from content experts in two ways. First, expert panel methods were used to elicit feedback on these concepts and to test the elicitation of terms for the vocabulary of the Values concept. Second, from these discussions we developed a mapping exercise to test the intuitiveness of the concepts, requesting that network leaders from four CLHSs complete a mapping exercise to associate characteristics of their networks with the high-level concepts, building the vocabulary for each concept in a grounded fashion. We also solicited feedback from these participants on the experience of completing the mapping exercise, finding that the exercise is acceptable and could aid in CLHS development and collaboration. Respondents identified opportunities to improve the operational definitions of each concept to ensure that corresponding vocabularies are distinct and non-overlapping., Discussion: Our results provide a foundation for developing a formal, explicit shared conceptualization of CLHSs. Once developed, such a tool can be useful for measurement, explanation, and improvement. Further work, including alignment to a top-level ontology, expanding the vocabulary, and defining relations between vocabulary is required to formally build out an ontology for these uses., Competing Interests: Michael Seid is an inventor of intellectual property licensed by CCHMC to Hive Networks, Inc., a for‐profit company that provides software and services to support learning networks. Alexandra H. Vinson, Breck Gamel, Shehzad Saeed, Brandy Fureman, Susan C. Cronin, Katherine Bates, and David Hartley have no disclosures to report., (© 2022 The Authors. Learning Health Systems published by Wiley Periodicals LLC on behalf of University of Michigan.)

Seizure documentation is an essential component of epilepsy management. Not all persons with epilepsy choose to document their seizures, but many view the practice as essential to managing their disease. While seizure documentation is a valuable aspect of patient care, clinicians and patients must remain aware that seizure underreport and overreport commonly occur due to lack of seizure awareness. Additionally, in rare cases, persons with epilepsy may intentionally conceal their seizures from clinicians. The continued development of electronic seizure diaries and epilepsy self-management software provides patients with new and expanding options for seizure documentation and disease management. In order for these tools to be utilized most effectively, patient input must be central to their development. Given the limitations of seizure documentation, the development of accurate, non-invasive seizure detection devices is crucial for accurate seizure monitoring., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jonathan Goldstein has no competing interests to report. Churl-Su Kwon has no competing interests to report. Michael Harmon has no competing interests to report. Alison Kukla has no competing interests to report. Jeffrey Buchhalter receives compensation for consulting work from Biocodex, the Epilepsy Foundation, Epilepsy Study Consortium, Epilog Care, Neurocrine Biosciences and UCB Pharma. Susan T. Herman receives grant support from the Epilepsy Foundation, NIH, Marinus Pharmaceuticals, and CREmedical, and compensation for consulting from BioSerenity. Susan McCallum has no competing interests to report. Lisa Raman has no competing interests to report. Brandy E. Fureman receives salary support from the Epilepsy Foundation, and grant funding from UCB Biopharma (Human Epilepsy Project 2), PCORI and the James M. Anderson Center for Health Systems Excellence (RI-PCC-2017 (sub: 03699), the CDC (1NU58DP006256-02-00), the National Association of Epilepsy Centers, and has no other conflicts of interest. Nathalie Jetté receives grant funding paid to her institution for grants unrelated to this work from NINDS (NIH U24NS107201, NIH IU54NS100064). She also receives an honorarium for her work as an Associate Editor of Epilepsia., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Competing Interests: Declaration of Conflicting Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Onset of many forms of epilepsy occurs after an initial epileptogenic insult or as a result of an identified genetic defect. Given that the precipitating insult is known, these epilepsies are, in principle, amenable to secondary prevention. However, development of preventive treatments is difficult because only a subset of individuals will develop epilepsy and we cannot currently predict which individuals are at the highest risk. Biomarkers that enable identification of these individuals would facilitate clinical trials of potential anti-epileptogenic treatments, but no such prognostic biomarkers currently exist. Several putative molecular, imaging, electroencephalographic and behavioural biomarkers of epileptogenesis have been identified, but clinical translation has been hampered by fragmented and poorly coordinated efforts, issues with inter-model reproducibility, study design and statistical approaches, and difficulties with validation in patients. These challenges demand a strategic roadmap to facilitate the identification, characterization and clinical validation of biomarkers for epileptogenesis. In this Review, we summarize the state of the art with respect to biomarker research in epileptogenesis and propose a five-phase roadmap, adapted from those developed for cancer and Alzheimer disease, that provides a conceptual structure for biomarker research.

Objective: To evaluate the impact of pediatric sleep disturbances and night-time seizure monitoring of children with rare epilepsy syndromes on the sleep quality and mental health of caregivers., Study Design: A cross-sectional study was conducted using caregiver entered data from the Rare Epilepsy Network on pediatric sleep disturbances and Patient Reported Outcomes Measurement Information System measures for caregiver fatigue, sleep disturbance, sleep-related impairment, depression, anxiety, companionship, and cognition. Logistic regression was used to examine associations between risk factors and caregiver sleep quality., Results: Non-Hispanic white mothers comprised 83% of the 742 respondents in this study. After adjusting for covariates, difficulty falling asleep, excessive daytime sleepiness, frequent night-time awakenings, and very restless sleep in children were associated with fatigue (aOR 95% CI, 1.5-2.2), sleep-related disturbance (aOR 95% CI, 1.7-2.6) and sleep impairment (aOR 95% CI, 1.5-2.4) in caregivers. Caregiver anxiety (aOR 95% CI, 3.6-6.0) and depression (aOR 95% CI, 2.8-6.0) were also highly associated with their fatigue and sleep quality, whereas companionship (aOR 95% CI, 0.3-0.4) and higher caregiver cognition (aOR 95% CI, 0.1-0.2) were protective. In addition, sharing a room or bed or using methods that require listening for seizures were significantly related to sleep disturbance and fatigue in the caregivers., Conclusions: In rare epilepsies, pediatric sleep disturbances and night-time seizure monitoring are significantly associated with caregiver fatigue and poor sleep quality. In addition to the intense caregiving needs of children with rare epilepsies, fatigue and poor sleep quality in caregivers may contribute to or result from mental health problems., (© 2020 Published by Elsevier Inc.)

Objective: The objective of the study was to identify functioning and quality-of-life (QOL) patient-reported outcome measurements (PROMs) feasible for use in the waiting room of adult epilepsy clinics., Material and Methods: We searched PubMed and Web of Science for articles on in English, Spanish, Portuguese, Italian, and French published by the end of February 15th, 2019. We screened retrieved titles and abstracts looking for publications that reported the use of PROMs to measure functioning and QOL in epilepsy. The authors, clinical experts, and patient advocates from the Epilepsy Foundation of America conceptualized a set of desirable feasibility attributes for PROMs implementation in the waiting room of adult epilepsy clinics. These attributes included brief time for completion (i.e., ≤3 min), free cost, coverage of four minimum QOL domains and respective facets, and good evidence of psychometric properties. We defined QOL domains according to the World Health Organization's classification and created psychometric appraisal criteria based on the Food and Drug Administration's (FDA) Guidance., Results: Eighteen candidate instruments were identified and compared with respect to desirable attributes for use in adult epilepsy clinics. We found that the Quality-of-life in epilepsy (QOLIE)-10 and Patient-Reported Outcome Measurement Information System-10 (PROMIS-10) were the most feasible PROMs for implementation in adult epilepsy clinics based on our criteria. The QOLIE-10 and PROMIS-10 still lack ideal evidence of responsiveness in people with epilepsy., Conclusion: This is the first systematic review that aimed to assess feasibility properties of available functioning and QOL PROMs. The QOLIE-10 and PROMIS-10 are potentially feasible instruments for implementation in the waiting room of adult epilepsy clinics. Further studies assessing the responsiveness of these PROMs are needed and will contribute to the selection of the most appropriate instrument for longitudinal use in adult epilepsy clinical practice., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The increased focus on stakeholder engagement in determining the aims, design, conduct of research and dissemination of results is substantially changing the biomedical research paradigm. In this era of patient-centered care, incorporating participatory action research methodology into large-scale multi-center studies is essential. The adoption of community engagement facilitates meaningful contribution to the design and implementation of clinical studies. Consequently, encouraging citizen participation and involving key organizations may guide the effective development of future clinical research protocols. Here, we discuss our experience in engaging individuals, their caregivers, as well as scientific and consumer organizations in public outreach and knowledge transfer to assist in the development of effective strategies for recruitment and retention in a future post-traumatic epilepsy prevention randomized controlled trial within the National Institute of Neurologic Disorders and Stroke Center Without Walls, Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx). The study includes a Public Engagement Core with a diverse consortium of stakeholder partners. Based on the Core's ongoing experience, it is recommended that multicenter studies integrate a participatory action research based approach to harness the benefits of a collective inquiry. The blueprint created by the EpiBioS4Rx Public Engagement Core is a resource that could be applied in other areas of biomedical research., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Objective: To describe the prevalence and characteristics of comorbidities in persons with rare epilepsies., Study Design: Persons with rare epilepsies and caregivers of those affected were recruited through the Epilepsy Foundation and more than 30 rare epilepsy advocacy organizations affiliated with the Rare Epilepsy Network (REN). A web-based survey was conducted using a questionnaire consisting of core sections to collect data from affected persons on various aspects, including comorbidities. Comorbidity information was grouped into 15 classes, 12 of which had a stem question followed by detailed branch questions and 3 that were created from a combination of related questions., Results: Of 795 persons with more than 30 different rare epilepsy diagnosis groups, one-half had ≥5 comorbidity classes and 97% were classified as complex chronic disease (C-CD). The highest number of comorbidity classes reported per person were persons with Aicardi syndrome, Phelan-McDermid syndrome (median, 7.0; IQR, 5.0-9.0), and tuberous sclerosis complex (median, 6.0; IQR, 4.0-8.0). The most common comorbidity classes were learning/developmental disability (71%), mental health issues (71%), sleep disorders (60%), brain abnormalities (52%), oral issues (49%), bone-joint issues (42%), hyper/hypotonia (42%), and eye-vision disorders (38%). The prevalence of brain abnormalities, hyper/hypotonia, eye, and cardiac disorders was significantly higher in persons first diagnosed with epilepsy at a younger age (<9 months) than in those first diagnosed at an older age (P < .05 for trend)., Conclusions: Nearly all persons with rare epilepsies are medically complex, with a high prevalence of multiple comorbidities, especially those who were diagnosed with epilepsy in the first year of life. Comorbidities should be carefully considered in the diagnosis and management of persons with rare epilepsies., (Copyright © 2018 Elsevier Inc. All rights reserved.)